Redundancy of the multi-subspaces′ fusion data represent by features can be minimized with the direct summation over multi-subspaces. In this paper, a new face recognition method based on feature fusion was proposed via using the direct summation of multi-subspaces. First we calculate the covariance matrices of all training samples′front face, left face and right face images, which are all normalized, and then calculate their first Plargest eigenvalues and corresponding mutually orthogonal eigenvectors, using the 2DPCA algorithm. Then we constitute three feature space (projection space) via three multi-subspaces′ orthogonal eigenvectors which meet the direct sum condition. The samples′ front face, left face and right face images are projected into the three spaces respectively. The projected features are fused as the classification feature. The comparison on the three groups of experimental results shows that our algorithm not only reduce the computation but also increase the recognition rate.